Retention modeling and method development in hydrophilic interaction chromatography

Abstract

In the present study, the possibility of retention modeling in the HILIC mode was investigated, testing several different literature relationships over a wide range of different analytical conditions (column chemistries and mobile phase pH) and using analytes possessing diverse physico-chemical properties. Furthermore, it was investigated how the retention prediction depends on the number of isocratic or gradient trial or initial scouting runs. The most promising set of scouting runs seems to be a combination of three isocratic runs (95, 90 and 70%ACN) and one gradient run (95 to 65%ACN in 10min), as the average prediction errors were lower than using six equally spaced isocratic runs and because it is common in Method development (MD) to perform at least one scouting gradient run in the screening step to find out the best column, temperature and pH conditions. Overall, the retention predictions were much less accurate in HILIC than what is usually experienced in RPLC. This has severe implications for MD, as it restricts the use of commercial software packages that require the simulation of the retention of every peak in the chromatogram. To overcome this problem, the recently proposed predictive elution window shifting and stretching (PEWS(2)) approach can be used. In this computer-assisted MD strategy, only an (approximate) prediction of the retention of the first and the last peak in the chromatogram is required to conduct a well-targeted trial-and-error search, with suggested search conditions uniformly covering the entire possible search and elution space. This strategy was used to optimize the separation of three representative pharmaceutical mixtures possessing diverse physico-chemical properties (pteridins, saccharides and cocktail of drugs/metabolites). All problems could be successfully handled in less than 2.5h of instrument time (including equilibration).

Keywords: HILIC; Method development; Predictive elution window shifting and stretching (PEWS(2)); Retention modeling; Retention prediction.